Qwen3.8 27B Q5_K_M + 7900xtx + DeepSeek Harness实战平均 52 t/s
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@exllm 并行改为1后，预填充性能有接近5~10倍的提升（不同的任务变化很大），但生成token的性能提升不到10%。我也将“--override-tensor blk.\d+.ffn_.*_exps.=CPU \”注释掉进行过测试，性能反而有略微下降，可能跟我的硬件和跑的任务也有一定关系。
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软硬件配置：
cpu： AMD Ryzen 5 5600
RAM： 32GB 3200
GPU： 7900xtx
OS： Ubuntu 26.04
软件 ： llama.cpp + vulkan
版本：version: 9737 (67e9fd3b7) built with GNU 15.2.0 for Linux x86_64测试：
- llama.cpp 自带webui 创建打飞机游戏
提示词：
"Create a complete, fully functional 2D space shooter arcade game inside a single HTML file using HTML5 Canvas, CSS, and vanilla JavaScript. **Requirements:** 1. **Canvas & Layout:** Set up a centered 800x600 black canvas with a retro arcade UI showing the current score and remaining lives at the top. 2. **Player Ship:** Draw a distinct player ship at the bottom center. Allow smooth movement left and right using the Arrow Keys or A/D keys, constrained within the canvas bounds. 3. **Shooting Mechanics:** Pressing the Spacebar should fire a laser projectile upward from the player's position. Implement a brief cooldown between shots. 4. **Enemies:** Spawn alien ships or asteroids at random X coordinates along the top, moving downward at varying speeds. 5. **Collision Detection:** Write precise collision logic for bullets hitting enemies (destroying both and adding points) and enemies hitting the player or bottom screen (losing a life). 6. **Game Loop & States:** Include smooth requestAnimationFrame logic, a 'Game Over' screen, and a 'Restart' button functionality. Clean, well-commented code only."日志
1.47.020.662 I slot print_timing: id 1 | task 0 | n_decoded = 5409, tg = 89.47 t/s, tg_3s = 71.71 t/s 1.47.476.673 I slot print_timing: id 1 | task 0 | prompt eval time = 519.21 ms / 262 tokens ( 1.98 ms per token, 504.61 tokens per second) 1.47.476.677 I slot print_timing: id 1 | task 0 | eval time = 60909.77 ms / 5440 tokens ( 11.20 ms per token, 89.31 tokens per second) 1.47.476.677 I slot print_timing: id 1 | task 0 | total time = 61428.98 ms / 5702 tokens 1.47.476.681 I slot print_timing: id 1 | task 0 | graphs reused = 1480 1.47.476.684 I slot print_timing: id 1 | task 0 | draft acceptance = 0.87497 ( 3940 accepted / 4503 generated), mean acceptance length = 3.62, acceptance rate per position = (0.943, 0.875, 0.806)- 配合 Deepseek Harness 修改QT + C++ 项目 SimulIDE， 使其能在apple silicon上运行，并修改Qt 文本框输入bug
llama.cpp 日志中的一段
32.32.489.805 I slot print_timing: id 1 | task 10430 | prompt eval time = 17284.15 ms / 4098 tokens ( 4.22 ms per token, 237.10 tokens per second) 32.32.489.807 I slot print_timing: id 1 | task 10430 | eval time = 5461.48 ms / 277 tokens ( 19.72 ms per token, 50.72 tokens per second) 32.32.489.808 I slot print_timing: id 1 | task 10430 | total time = 22745.63 ms / 4375 tokens 32.32.489.808 I slot print_timing: id 1 | task 10430 | graphs reused = 9929 32.32.489.811 I slot print_timing: id 1 | task 10430 | draft acceptance = 0.89333 ( 201 accepted / 225 generated), mean acceptance length = 3.68, acceptance rate per position = (0.960, 0.893, 0.827)DSH 摘要：
63歩ILLM 13m13S・工具週用 2m2s｜首token 平均3.8s・52 tok/s|緩存命中98%1輸入 4.4M tok・輸出 29.2K tokllama.cpp运行参数：
-t 10 -b 512 -ub 256 --spec-draft-n-max 3 --fit off --no-context-shift --metrics --kv-unified --jinja --cache-type-k q8_0 --cache-type-v q8_0 -fa on --spec-type draft-mtp --ctx-size 131072 --parallel 2 -ngl -1 --host 0.0.0.0 --port 8080 --chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"} --no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0 --override-tensor blk\.\d+\.ffn_.*_exps\.=CPU --alias qwen3.8-27b-q5@exllm @terry
根据楼主的测试了一下
cpu： AMD Ryzen 5 7500F
RAM：DDR5 32GB 6000
GPU： 7900XTX
OS： Ubuntu 24.04
软件 ： llama.cpp + vulkan先说结果：
llama.cpp运行参数，spec-draft-n-max 3 速度最好：~/llama_vulkan/bin/llama-server \ -m ~/models/Qwen3.8-27B-Q5_K_M.gguf \ -t 10 \ -b 512 \ -ub 256 \ --spec-draft-n-max 3 \ --fit off \ --no-context-shift \ --metrics \ --kv-unified \ --jinja \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ -fa on \ --spec-type draft-mtp \ --ctx-size 131072 \ --parallel 1 \ -ngl -1 \ --host 0.0.0.0 \ --port 8080 \ --chat-template-kwargs '{"enable_thinking":true,"preserve_think":false,"reasoning_effort":"medium"}' \ --no-mmap \ --temp 0.6 \ --top-p 0.5 \ --top-k 15 \ --repeat-penalty 1.0 \ --alias qwen3.8-27b-q5llama.cpp运行参数，spec-draft-n-max 2 ：
llama.cpp运行参数，spec-draft-n-max 4 ：
总结：
参数 Prompt速度 生成速度 平均延迟 MTP命中率 n=2 288.73 t/s 68.14 t/s 17.436 s 87.62% n=3 286.60 t/s 71.74 t/s 16.772 s 82.50% n=4 249.53 t/s 47.96 t/s 24.911 s 74.99% 这台电脑n=3最好。
注：修改这个参数是MTP speculative decoding（推测解码）一次最多提前“猜”多少个 token。 - llama.cpp 自带webui 创建打飞机游戏
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为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
此主題已被删除！
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感谢楼主. 7900xtx 能跑,不错. 明天试试dsh. 今天dsh+v4pro花了100多, 肉疼(不过确实强, 长链自主调试FPGA, 综合+ila probe+烧写+测试+uart输出分析一条龙, 我基本可以不用管, 花token就行).
bruin@lmde7 ~ $ ./run-3.8-q5.sh 0.00.035.069 W Setting 'enable_thinking' via --chat-template-kwargs is deprecated. Use --reasoning on / --reasoning off instead. 0.00.035.097 W DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead 0.00.039.005 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.039.491 W srv llama_server: ----------------- 0.00.039.495 W srv llama_server: CORS is set to allow all origins ('*') and no API key is set 0.00.039.495 W srv llama_server: this can be a security risk (cross-origin attacks) 0.00.039.495 W srv llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655 0.00.039.495 W srv llama_server: ----------------- 0.00.040.765 I srv load_model: loading model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.08.836.391 I cmn init: llama threadpool init, n_threads = 10 0.09.247.607 I common_speculative_init_result: creating MTP draft context against the target model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.09.318.357 I srv load_model: initializing, n_slots = 1, n_ctx_slot = 131072, kv_unified = 'true' 0.09.457.688 I srv init: chat template supports preserving reasoning, consider enabling it via --reasoning-preserve 0.09.457.746 I srv llama_server: model loaded 0.09.457.750 I srv llama_server: listening on http://0.0.0.0:8000 0.31.502.907 I slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1 0.31.503.144 I slot launch_slot_: id 0 | task 0 | processing task, is_child = 0 0.33.674.917 I slot print_timing: id 0 | task 0 | prompt eval time = 1607.08 ms / 333 tokens ( 4.83 ms per token, 207.21 tokens per second) 0.33.674.922 I slot print_timing: id 0 | task 0 | eval time = 564.47 ms / 34 tokens ( 17.11 ms per token, 58.46 tokens per second) 0.33.674.923 I slot print_timing: id 0 | task 0 | total time = 2171.56 ms / 367 tokens 0.33.674.928 I slot print_timing: id 0 | task 0 | graphs reused = 11 0.33.674.930 I slot print_timing: id 0 | task 0 | draft acceptance = 0.72727 ( 24 accepted / 33 generated), mean len = 3.18 0.33.674.999 I slot release: id 0 | task 0 | stop processing: n_tokens = 368, truncated = 0 0.39.250.971 I slot get_availabl: id 0 | task -1 | selected slot by LCP similarity, f_sim_best = 0.963 (> 0.100 thold), f_keep = 1.000 0.39.251.208 I slot launch_slot_: id 0 | task 16 | processing task, is_child = 0 0.40.592.295 I slot print_timing: id 0 | task 16 | prompt eval time = 368.90 ms / 14 tokens ( 26.35 ms per token, 37.95 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | eval time = 972.01 ms / 67 tokens ( 14.73 ms per token, 67.90 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | total time = 1340.91 ms / 81 tokens 0.40.592.305 I slot print_timing: id 0 | task 16 | graphs reused = 31 0.40.592.307 I slot print_timing: id 0 | task 16 | draft acceptance = 0.71429 ( 45 accepted / 63 generated), mean len = 3.14 0.40.592.367 I slot release: id 0 | task 16 | stop processing: n_tokens = 448, truncated = 0llama.cpp 我用的最新 b10485; 完全抄作业:
bruin@lmde7 ~ $ cat run-3.8-q5.sh #!/bin/bash # ref: https://lcz.me/topic/1157/qwen3.8-27b-q5_k_m-7900xtx-deepseek-harness%E5%AE%9E%E6%88%98%E5%B9%B3%E5%9D%87-52-t-s LLAMA_SERVER=/home/bruin/llama-server-vulkan-b10485 MAIN_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf" MTMD_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/mmproj-F16.gguf" #--mmproj "${MTMD_MODEL}" \ ${LLAMA_SERVER} \ --device Vulkan0 \ --model "${MAIN_MODEL}" \ -t 10 \ -b 512 \ -ub 256 \ --spec-draft-n-max 3 \ --fit off \ --no-context-shift \ --metrics \ --kv-unified \ --jinja \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ -fa on \ --spec-type draft-mtp \ --ctx-size 131072 \ --parallel 1 \ -ngl -1 \ --host 0.0.0.0 \ --port 8000 \ --chat-template-kwargs '{"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}' \ --no-mmap \ --temp 0.6 \ --top-p 0.5 \ --top-k 15 \ --repeat-penalty 1.0 \ --override-tensor blk\.\d+\.ffn_.*_exps\.=CPU \ --alias qwen3.8-27b-q5
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感谢楼主. 7900xtx 能跑,不错. 明天试试dsh. 今天dsh+v4pro花了100多, 肉疼(不过确实强, 长链自主调试FPGA, 综合+ila probe+烧写+测试+uart输出分析一条龙, 我基本可以不用管, 花token就行).
bruin@lmde7 ~ $ ./run-3.8-q5.sh 0.00.035.069 W Setting 'enable_thinking' via --chat-template-kwargs is deprecated. Use --reasoning on / --reasoning off instead. 0.00.035.097 W DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead 0.00.039.005 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.039.491 W srv llama_server: ----------------- 0.00.039.495 W srv llama_server: CORS is set to allow all origins ('*') and no API key is set 0.00.039.495 W srv llama_server: this can be a security risk (cross-origin attacks) 0.00.039.495 W srv llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655 0.00.039.495 W srv llama_server: ----------------- 0.00.040.765 I srv load_model: loading model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.08.836.391 I cmn init: llama threadpool init, n_threads = 10 0.09.247.607 I common_speculative_init_result: creating MTP draft context against the target model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.09.318.357 I srv load_model: initializing, n_slots = 1, n_ctx_slot = 131072, kv_unified = 'true' 0.09.457.688 I srv init: chat template supports preserving reasoning, consider enabling it via --reasoning-preserve 0.09.457.746 I srv llama_server: model loaded 0.09.457.750 I srv llama_server: listening on http://0.0.0.0:8000 0.31.502.907 I slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1 0.31.503.144 I slot launch_slot_: id 0 | task 0 | processing task, is_child = 0 0.33.674.917 I slot print_timing: id 0 | task 0 | prompt eval time = 1607.08 ms / 333 tokens ( 4.83 ms per token, 207.21 tokens per second) 0.33.674.922 I slot print_timing: id 0 | task 0 | eval time = 564.47 ms / 34 tokens ( 17.11 ms per token, 58.46 tokens per second) 0.33.674.923 I slot print_timing: id 0 | task 0 | total time = 2171.56 ms / 367 tokens 0.33.674.928 I slot print_timing: id 0 | task 0 | graphs reused = 11 0.33.674.930 I slot print_timing: id 0 | task 0 | draft acceptance = 0.72727 ( 24 accepted / 33 generated), mean len = 3.18 0.33.674.999 I slot release: id 0 | task 0 | stop processing: n_tokens = 368, truncated = 0 0.39.250.971 I slot get_availabl: id 0 | task -1 | selected slot by LCP similarity, f_sim_best = 0.963 (> 0.100 thold), f_keep = 1.000 0.39.251.208 I slot launch_slot_: id 0 | task 16 | processing task, is_child = 0 0.40.592.295 I slot print_timing: id 0 | task 16 | prompt eval time = 368.90 ms / 14 tokens ( 26.35 ms per token, 37.95 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | eval time = 972.01 ms / 67 tokens ( 14.73 ms per token, 67.90 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | total time = 1340.91 ms / 81 tokens 0.40.592.305 I slot print_timing: id 0 | task 16 | graphs reused = 31 0.40.592.307 I slot print_timing: id 0 | task 16 | draft acceptance = 0.71429 ( 45 accepted / 63 generated), mean len = 3.14 0.40.592.367 I slot release: id 0 | task 16 | stop processing: n_tokens = 448, truncated = 0llama.cpp 我用的最新 b10485; 完全抄作业:
bruin@lmde7 ~ $ cat run-3.8-q5.sh #!/bin/bash # ref: https://lcz.me/topic/1157/qwen3.8-27b-q5_k_m-7900xtx-deepseek-harness%E5%AE%9E%E6%88%98%E5%B9%B3%E5%9D%87-52-t-s LLAMA_SERVER=/home/bruin/llama-server-vulkan-b10485 MAIN_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf" MTMD_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/mmproj-F16.gguf" #--mmproj "${MTMD_MODEL}" \ ${LLAMA_SERVER} \ --device Vulkan0 \ --model "${MAIN_MODEL}" \ -t 10 \ -b 512 \ -ub 256 \ --spec-draft-n-max 3 \ --fit off \ --no-context-shift \ --metrics \ --kv-unified \ --jinja \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ -fa on \ --spec-type draft-mtp \ --ctx-size 131072 \ --parallel 1 \ -ngl -1 \ --host 0.0.0.0 \ --port 8000 \ --chat-template-kwargs '{"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}' \ --no-mmap \ --temp 0.6 \ --top-p 0.5 \ --top-k 15 \ --repeat-penalty 1.0 \ --override-tensor blk\.\d+\.ffn_.*_exps\.=CPU \ --alias qwen3.8-27b-q5@laobenxiong 确实，用了你这个q5ks的模型，我的速度终于正常了，也不用关thinking，不知道为什么其他人的q5km可以
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关于--parallel 2 的测试
软硬件环境：
cpu： AMD Ryzen 7 9700X
RAM：DDR5 64GB 6000
GPU：R9700 32G
OS： Ubuntu 24.04
软件 ： llama.cpp + vulkan结论：将并行设为1或者2，大部分情况下，性能变化属于正常波动，没有明显差别。
但是，即使有32G显存，有些任务还是会报以下错误：
26.48.401.777 E init_batch: failed to prepare attention ubatches
26.48.401.778 W decode: failed to find a memory slot for batch of size 16
26.48.401.779 W srv decode: failed to find free space in the KV cache, retrying with smaller batch size, off = 190, n_batch = 8, ret = 1
26.48.403.275 E init_batch: failed to prepare attention ubatches
26.48.403.277 W decode: failed to find a memory slot for batch of size 8
26.48.403.277 W srv decode: failed to find free space in the KV cache, retrying with smaller batch size, off = 190, n_batch = 4, ret = 1
虽然使用了 --cache-type-k/v q8_0 做 KV 量化来节省显存，但是 128K 的超长上下文，加上 --parallel 2，对 KV Cache 的要求依然极其恐怖。模型权重稳稳地占了 17GB。但当开启并发，且 Prompt 超长时，给 128K 上下文预留的 KV Cache 空间，在高峰期（特别是并发请求时）瞬间耗尽了，导致没有空位给下一个 Token 的生成。
好消息是，llama.cpp不能为KV Cache分配空间，并不会导致崩溃或任务中断（我没有碰到过），只会导致llama.cpp降级重试，严重的情况下会感觉明显卡顿而已，不用过于担心。
所以，用7900XTX的朋友，把并发设为1可能是更好的选择。 -
，系统 取消固定了此主题
